
where \(\phi _i^0 = \theta \)  and \(\alpha \)  is the learning rate. Now, let us see the importance of the starting point \(\theta \)  for each task in more detail. The starting point actually controls the number of iterations required to converge to the local optima \(\phi _i^\star (\theta )\) . If the local function is strongly convex, then the number of iterations required for \(\epsilon \) -optimal solution is given by [1]}
\(K_i\ge \mathcal {O}\left(\frac{\kappa }{\epsilon }\log \left(\frac{\mathcal {L}(\theta , \mathcal {D}_i^{tr})-\mathcal {L}(\phi _i^{K_i}(\theta ), \mathcal {D}_i^{tr})}{\epsilon }\right)\right).\) 
